Overview

Dataset statistics

Number of variables22
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory707.0 KiB
Average record size in memory724.0 B

Variable types

Numeric8
Categorical13
Boolean1

Alerts

auto_make is highly overall correlated with auto_modelHigh correlation
auto_model is highly overall correlated with auto_makeHigh correlation
injury_claim is highly overall correlated with property_claim and 2 other fieldsHigh correlation
property_claim is highly overall correlated with injury_claim and 2 other fieldsHigh correlation
total_claim_amount is highly overall correlated with injury_claim and 2 other fieldsHigh correlation
vehicle_claim is highly overall correlated with injury_claim and 2 other fieldsHigh correlation
umbrella_limit has 798 (79.8%) zerosZeros
injury_claim has 25 (2.5%) zerosZeros
property_claim has 19 (1.9%) zerosZeros

Reproduction

Analysis started2025-12-29 00:40:17.911425
Analysis finished2025-12-29 00:40:23.916552
Duration6.01 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

policy_annual_premium
Real number (ℝ)

Distinct991
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1256.4061
Minimum433.33
Maximum2047.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-12-28T18:40:23.956333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum433.33
5-th percentile855.112
Q11089.6075
median1257.2
Q31415.695
95-th percentile1653.4435
Maximum2047.59
Range1614.26
Interquartile range (IQR)326.0875

Descriptive statistics

Standard deviation244.16739
Coefficient of variation (CV)0.19433795
Kurtosis0.07388944
Mean1256.4061
Median Absolute Deviation (MAD)164.26
Skewness0.0044019945
Sum1256406.1
Variance59617.717
MonotonicityNot monotonic
2025-12-28T18:40:24.026778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1558.29 2
 
0.2%
1215.36 2
 
0.2%
1362.87 2
 
0.2%
1073.83 2
 
0.2%
1389.13 2
 
0.2%
1074.07 2
 
0.2%
1374.22 2
 
0.2%
1524.45 2
 
0.2%
1281.25 2
 
0.2%
1230.69 1
 
0.1%
Other values (981) 981
98.1%
ValueCountFrequency (%)
433.33 1
0.1%
484.67 1
0.1%
538.17 1
0.1%
566.11 1
0.1%
617.11 1
0.1%
625.08 1
0.1%
653.66 1
0.1%
664.86 1
0.1%
671.01 1
0.1%
671.92 1
0.1%
ValueCountFrequency (%)
2047.59 1
0.1%
1969.63 1
0.1%
1935.85 1
0.1%
1927.87 1
0.1%
1922.84 1
0.1%
1896.91 1
0.1%
1878.44 1
0.1%
1865.83 1
0.1%
1863.04 1
0.1%
1861.43 1
0.1%
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size59.4 KiB
1000
351 
500
342 
2000
307 

Length

Max length4
Median length4
Mean length3.658
Min length3

Characters and Unicode

Total characters3658
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1000
2nd row2000
3rd row2000
4th row2000
5th row1000

Common Values

ValueCountFrequency (%)
1000 351
35.1%
500 342
34.2%
2000 307
30.7%

Length

2025-12-28T18:40:24.090824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-28T18:40:24.149489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1000 351
35.1%
500 342
34.2%
2000 307
30.7%

Most occurring characters

ValueCountFrequency (%)
0 2658
72.7%
1 351
 
9.6%
5 342
 
9.3%
2 307
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3658
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2658
72.7%
1 351
 
9.6%
5 342
 
9.3%
2 307
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Common 3658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2658
72.7%
1 351
 
9.6%
5 342
 
9.3%
2 307
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2658
72.7%
1 351
 
9.6%
5 342
 
9.3%
2 307
 
8.4%

umbrella_limit
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1101000
Minimum-1000000
Maximum10000000
Zeros798
Zeros (%)79.8%
Negative1
Negative (%)0.1%
Memory size7.9 KiB
2025-12-28T18:40:24.193316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1000000
5-th percentile0
Q10
median0
Q30
95-th percentile6000000
Maximum10000000
Range11000000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2297406.6
Coefficient of variation (CV)2.0866545
Kurtosis1.7920773
Mean1101000
Median Absolute Deviation (MAD)0
Skewness1.8067122
Sum1.101 × 109
Variance5.2780771 × 1012
MonotonicityNot monotonic
2025-12-28T18:40:24.237883image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 798
79.8%
6000000 57
 
5.7%
5000000 46
 
4.6%
4000000 39
 
3.9%
7000000 29
 
2.9%
3000000 12
 
1.2%
8000000 8
 
0.8%
9000000 5
 
0.5%
2000000 3
 
0.3%
10000000 2
 
0.2%
ValueCountFrequency (%)
-1000000 1
 
0.1%
0 798
79.8%
2000000 3
 
0.3%
3000000 12
 
1.2%
4000000 39
 
3.9%
5000000 46
 
4.6%
6000000 57
 
5.7%
7000000 29
 
2.9%
8000000 8
 
0.8%
9000000 5
 
0.5%
ValueCountFrequency (%)
10000000 2
 
0.2%
9000000 5
 
0.5%
8000000 8
 
0.8%
7000000 29
 
2.9%
6000000 57
 
5.7%
5000000 46
 
4.6%
4000000 39
 
3.9%
3000000 12
 
1.2%
2000000 3
 
0.3%
0 798
79.8%

insured_age
Real number (ℝ)

Distinct46
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.948
Minimum19
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-12-28T18:40:24.296674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile26
Q132
median38
Q344
95-th percentile57
Maximum64
Range45
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.1402867
Coefficient of variation (CV)0.23467923
Kurtosis-0.26025502
Mean38.948
Median Absolute Deviation (MAD)6
Skewness0.47898805
Sum38948
Variance83.544841
MonotonicityNot monotonic
2025-12-28T18:40:24.358807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
43 49
 
4.9%
39 48
 
4.8%
41 45
 
4.5%
34 44
 
4.4%
38 42
 
4.2%
30 42
 
4.2%
31 42
 
4.2%
37 41
 
4.1%
33 39
 
3.9%
40 38
 
3.8%
Other values (36) 570
57.0%
ValueCountFrequency (%)
19 1
 
0.1%
20 1
 
0.1%
21 6
 
0.6%
22 1
 
0.1%
23 7
 
0.7%
24 10
 
1.0%
25 14
1.4%
26 26
2.6%
27 24
2.4%
28 30
3.0%
ValueCountFrequency (%)
64 2
 
0.2%
63 2
 
0.2%
62 4
 
0.4%
61 10
1.0%
60 9
0.9%
59 5
 
0.5%
58 8
0.8%
57 16
1.6%
56 8
0.8%
55 14
1.4%

insured_sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size60.7 KiB
FEMALE
537 
MALE
463 

Length

Max length6
Median length6
Mean length5.074
Min length4

Characters and Unicode

Total characters5074
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALE
2nd rowMALE
3rd rowFEMALE
4th rowFEMALE
5th rowMALE

Common Values

ValueCountFrequency (%)
FEMALE 537
53.7%
MALE 463
46.3%

Length

2025-12-28T18:40:24.417467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-28T18:40:24.469134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
female 537
53.7%
male 463
46.3%

Most occurring characters

ValueCountFrequency (%)
E 1537
30.3%
M 1000
19.7%
A 1000
19.7%
L 1000
19.7%
F 537
 
10.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5074
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1537
30.3%
M 1000
19.7%
A 1000
19.7%
L 1000
19.7%
F 537
 
10.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5074
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1537
30.3%
M 1000
19.7%
A 1000
19.7%
L 1000
19.7%
F 537
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5074
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 1537
30.3%
M 1000
19.7%
A 1000
19.7%
L 1000
19.7%
F 537
 
10.6%
Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size61.6 KiB
JD
161 
High School
160 
Associate
145 
MD
144 
Masters
143 
Other values (2)
247 

Length

Max length11
Median length9
Mean length5.905
Min length2

Characters and Unicode

Total characters5905
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMD
2nd rowMD
3rd rowPhD
4th rowPhD
5th rowAssociate

Common Values

ValueCountFrequency (%)
JD 161
16.1%
High School 160
16.0%
Associate 145
14.5%
MD 144
14.4%
Masters 143
14.3%
PhD 125
12.5%
College 122
12.2%

Length

2025-12-28T18:40:24.510445image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-28T18:40:24.645780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
jd 161
13.9%
high 160
13.8%
school 160
13.8%
associate 145
12.5%
md 144
12.4%
masters 143
12.3%
phd 125
10.8%
college 122
10.5%

Most occurring characters

ValueCountFrequency (%)
o 587
 
9.9%
s 576
 
9.8%
e 532
 
9.0%
h 445
 
7.5%
D 430
 
7.3%
l 404
 
6.8%
i 305
 
5.2%
c 305
 
5.2%
t 288
 
4.9%
a 288
 
4.9%
Other values (10) 1745
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4155
70.4%
Uppercase Letter 1590
 
26.9%
Space Separator 160
 
2.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 587
14.1%
s 576
13.9%
e 532
12.8%
h 445
10.7%
l 404
9.7%
i 305
7.3%
c 305
7.3%
t 288
6.9%
a 288
6.9%
g 282
6.8%
Uppercase Letter
ValueCountFrequency (%)
D 430
27.0%
M 287
18.1%
J 161
 
10.1%
S 160
 
10.1%
H 160
 
10.1%
A 145
 
9.1%
P 125
 
7.9%
C 122
 
7.7%
Space Separator
ValueCountFrequency (%)
160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5745
97.3%
Common 160
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 587
 
10.2%
s 576
 
10.0%
e 532
 
9.3%
h 445
 
7.7%
D 430
 
7.5%
l 404
 
7.0%
i 305
 
5.3%
c 305
 
5.3%
t 288
 
5.0%
a 288
 
5.0%
Other values (9) 1585
27.6%
Common
ValueCountFrequency (%)
160
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 587
 
9.9%
s 576
 
9.8%
e 532
 
9.0%
h 445
 
7.5%
D 430
 
7.3%
l 404
 
6.8%
i 305
 
5.2%
c 305
 
5.2%
t 288
 
4.9%
a 288
 
4.9%
Other values (10) 1745
29.6%
Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size69.0 KiB
machine-op-inspct
93 
prof-specialty
85 
tech-support
78 
sales
76 
exec-managerial
76 
Other values (9)
592 

Length

Max length17
Median length16
Mean length13.521
Min length5

Characters and Unicode

Total characters13521
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcraft-repair
2nd rowmachine-op-inspct
3rd rowsales
4th rowarmed-forces
5th rowsales

Common Values

ValueCountFrequency (%)
machine-op-inspct 93
 
9.3%
prof-specialty 85
 
8.5%
tech-support 78
 
7.8%
sales 76
 
7.6%
exec-managerial 76
 
7.6%
craft-repair 74
 
7.4%
transport-moving 72
 
7.2%
other-service 71
 
7.1%
priv-house-serv 71
 
7.1%
armed-forces 69
 
6.9%
Other values (4) 235
23.5%

Length

2025-12-28T18:40:24.706611image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
machine-op-inspct 93
 
9.3%
prof-specialty 85
 
8.5%
tech-support 78
 
7.8%
sales 76
 
7.6%
exec-managerial 76
 
7.6%
craft-repair 74
 
7.4%
transport-moving 72
 
7.2%
other-service 71
 
7.1%
priv-house-serv 71
 
7.1%
armed-forces 69
 
6.9%
Other values (4) 235
23.5%

Most occurring characters

ValueCountFrequency (%)
e 1543
11.4%
r 1379
10.2%
- 1088
 
8.0%
a 1062
 
7.9%
s 986
 
7.3%
i 922
 
6.8%
c 886
 
6.6%
p 792
 
5.9%
t 749
 
5.5%
o 674
 
5.0%
Other values (11) 3440
25.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12433
92.0%
Dash Punctuation 1088
 
8.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1543
12.4%
r 1379
11.1%
a 1062
 
8.5%
s 986
 
7.9%
i 922
 
7.4%
c 886
 
7.1%
p 792
 
6.4%
t 749
 
6.0%
o 674
 
5.4%
n 620
 
5.0%
Other values (10) 2820
22.7%
Dash Punctuation
ValueCountFrequency (%)
- 1088
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12433
92.0%
Common 1088
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1543
12.4%
r 1379
11.1%
a 1062
 
8.5%
s 986
 
7.9%
i 922
 
7.4%
c 886
 
7.1%
p 792
 
6.4%
t 749
 
6.0%
o 674
 
5.4%
n 620
 
5.0%
Other values (10) 2820
22.7%
Common
ValueCountFrequency (%)
- 1088
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13521
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1543
11.4%
r 1379
10.2%
- 1088
 
8.0%
a 1062
 
7.9%
s 986
 
7.3%
i 922
 
6.8%
c 886
 
6.6%
p 792
 
5.9%
t 749
 
5.5%
o 674
 
5.0%
Other values (11) 3440
25.4%
Distinct6
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size65.0 KiB
own-child
183 
other-relative
177 
not-in-family
174 
husband
170 
wife
155 

Length

Max length14
Median length13
Mean length9.466
Min length4

Characters and Unicode

Total characters9466
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhusband
2nd rowother-relative
3rd rowown-child
4th rowunmarried
5th rowunmarried

Common Values

ValueCountFrequency (%)
own-child 183
18.3%
other-relative 177
17.7%
not-in-family 174
17.4%
husband 170
17.0%
wife 155
15.5%
unmarried 141
14.1%

Length

2025-12-28T18:40:24.758980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-28T18:40:24.815473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
own-child 183
18.3%
other-relative 177
17.7%
not-in-family 174
17.4%
husband 170
17.0%
wife 155
15.5%
unmarried 141
14.1%

Most occurring characters

ValueCountFrequency (%)
i 1004
 
10.6%
n 842
 
8.9%
e 827
 
8.7%
- 708
 
7.5%
a 662
 
7.0%
r 636
 
6.7%
l 534
 
5.6%
o 534
 
5.6%
h 530
 
5.6%
t 528
 
5.6%
Other values (10) 2661
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8758
92.5%
Dash Punctuation 708
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 1004
11.5%
n 842
 
9.6%
e 827
 
9.4%
a 662
 
7.6%
r 636
 
7.3%
l 534
 
6.1%
o 534
 
6.1%
h 530
 
6.1%
t 528
 
6.0%
d 494
 
5.6%
Other values (9) 2167
24.7%
Dash Punctuation
ValueCountFrequency (%)
- 708
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8758
92.5%
Common 708
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 1004
11.5%
n 842
 
9.6%
e 827
 
9.4%
a 662
 
7.6%
r 636
 
7.3%
l 534
 
6.1%
o 534
 
6.1%
h 530
 
6.1%
t 528
 
6.0%
d 494
 
5.6%
Other values (9) 2167
24.7%
Common
ValueCountFrequency (%)
- 708
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9466
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 1004
 
10.6%
n 842
 
8.9%
e 827
 
8.7%
- 708
 
7.5%
a 662
 
7.0%
r 636
 
6.7%
l 534
 
5.6%
o 534
 
5.6%
h 530
 
5.6%
t 528
 
5.6%
Other values (10) 2661
28.1%

insured_hobbies
Categorical

Distinct20
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size63.7 KiB
reading
 
64
exercise
 
57
paintball
 
57
bungie-jumping
 
56
movies
 
55
Other values (15)
711 

Length

Max length14
Median length11
Mean length8.113
Min length4

Characters and Unicode

Total characters8113
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsleeping
2nd rowreading
3rd rowboard-games
4th rowboard-games
5th rowboard-games

Common Values

ValueCountFrequency (%)
reading 64
 
6.4%
exercise 57
 
5.7%
paintball 57
 
5.7%
bungie-jumping 56
 
5.6%
movies 55
 
5.5%
golf 55
 
5.5%
camping 55
 
5.5%
kayaking 54
 
5.4%
yachting 53
 
5.3%
hiking 52
 
5.2%
Other values (10) 442
44.2%

Length

2025-12-28T18:40:24.872430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
reading 64
 
6.4%
exercise 57
 
5.7%
paintball 57
 
5.7%
bungie-jumping 56
 
5.6%
movies 55
 
5.5%
golf 55
 
5.5%
camping 55
 
5.5%
kayaking 54
 
5.4%
yachting 53
 
5.3%
hiking 52
 
5.2%
Other values (10) 442
44.2%

Most occurring characters

ValueCountFrequency (%)
i 927
 
11.4%
g 725
 
8.9%
e 705
 
8.7%
a 700
 
8.6%
n 672
 
8.3%
s 545
 
6.7%
o 337
 
4.2%
l 325
 
4.0%
m 313
 
3.9%
p 305
 
3.8%
Other values (14) 2559
31.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7875
97.1%
Dash Punctuation 238
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 927
 
11.8%
g 725
 
9.2%
e 705
 
9.0%
a 700
 
8.9%
n 672
 
8.5%
s 545
 
6.9%
o 337
 
4.3%
l 325
 
4.1%
m 313
 
4.0%
p 305
 
3.9%
Other values (13) 2321
29.5%
Dash Punctuation
ValueCountFrequency (%)
- 238
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7875
97.1%
Common 238
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 927
 
11.8%
g 725
 
9.2%
e 705
 
9.0%
a 700
 
8.9%
n 672
 
8.5%
s 545
 
6.9%
o 337
 
4.3%
l 325
 
4.1%
m 313
 
4.0%
p 305
 
3.9%
Other values (13) 2321
29.5%
Common
ValueCountFrequency (%)
- 238
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8113
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 927
 
11.4%
g 725
 
8.9%
e 705
 
8.7%
a 700
 
8.6%
n 672
 
8.3%
s 545
 
6.7%
o 337
 
4.2%
l 325
 
4.0%
m 313
 
3.9%
p 305
 
3.8%
Other values (14) 2559
31.5%

auto_make
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size61.4 KiB
Saab
80 
Dodge
80 
Suburu
80 
Nissan
78 
Chevrolet
76 
Other values (9)
606 

Length

Max length10
Median length9
Mean length5.703
Min length3

Characters and Unicode

Total characters5703
Distinct characters33
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSaab
2nd rowMercedes
3rd rowDodge
4th rowChevrolet
5th rowAccura

Common Values

ValueCountFrequency (%)
Saab 80
 
8.0%
Dodge 80
 
8.0%
Suburu 80
 
8.0%
Nissan 78
 
7.8%
Chevrolet 76
 
7.6%
Ford 72
 
7.2%
BMW 72
 
7.2%
Toyota 70
 
7.0%
Audi 69
 
6.9%
Accura 68
 
6.8%
Other values (4) 255
25.5%

Length

2025-12-28T18:40:24.925239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
saab 80
 
8.0%
dodge 80
 
8.0%
suburu 80
 
8.0%
nissan 78
 
7.8%
chevrolet 76
 
7.6%
ford 72
 
7.2%
bmw 72
 
7.2%
toyota 70
 
7.0%
audi 69
 
6.9%
accura 68
 
6.8%
Other values (4) 255
25.5%

Most occurring characters

ValueCountFrequency (%)
e 629
 
11.0%
a 499
 
8.7%
o 491
 
8.6%
u 377
 
6.6%
r 361
 
6.3%
d 341
 
6.0%
s 289
 
5.1%
c 201
 
3.5%
n 201
 
3.5%
S 160
 
2.8%
Other values (23) 2154
37.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4559
79.9%
Uppercase Letter 1144
 
20.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 629
13.8%
a 499
10.9%
o 491
10.8%
u 377
 
8.3%
r 361
 
7.9%
d 341
 
7.5%
s 289
 
6.3%
c 201
 
4.4%
n 201
 
4.4%
b 160
 
3.5%
Other values (10) 1010
22.2%
Uppercase Letter
ValueCountFrequency (%)
S 160
14.0%
M 137
12.0%
A 137
12.0%
D 80
7.0%
N 78
6.8%
C 76
 
6.6%
B 72
 
6.3%
F 72
 
6.3%
W 72
 
6.3%
T 70
 
6.1%
Other values (3) 190
16.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 5703
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 629
 
11.0%
a 499
 
8.7%
o 491
 
8.6%
u 377
 
6.6%
r 361
 
6.3%
d 341
 
6.0%
s 289
 
5.1%
c 201
 
3.5%
n 201
 
3.5%
S 160
 
2.8%
Other values (23) 2154
37.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5703
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 629
 
11.0%
a 499
 
8.7%
o 491
 
8.6%
u 377
 
6.6%
r 361
 
6.3%
d 341
 
6.0%
s 289
 
5.1%
c 201
 
3.5%
n 201
 
3.5%
S 160
 
2.8%
Other values (23) 2154
37.8%

auto_model
Categorical

HIGH CORRELATION 

Distinct39
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size60.8 KiB
RAM
 
43
Wrangler
 
42
A3
 
37
Neon
 
37
MDX
 
36
Other values (34)
805 

Length

Max length14
Median length9
Mean length5.178
Min length2

Characters and Unicode

Total characters5178
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row92x
2nd rowE400
3rd rowRAM
4th rowTahoe
5th rowRSX

Common Values

ValueCountFrequency (%)
RAM 43
 
4.3%
Wrangler 42
 
4.2%
A3 37
 
3.7%
Neon 37
 
3.7%
MDX 36
 
3.6%
Jetta 35
 
3.5%
Passat 33
 
3.3%
A5 32
 
3.2%
Legacy 32
 
3.2%
Pathfinder 31
 
3.1%
Other values (29) 642
64.2%

Length

2025-12-28T18:40:24.981277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ram 43
 
4.1%
wrangler 42
 
4.0%
a3 37
 
3.5%
neon 37
 
3.5%
mdx 36
 
3.5%
jetta 35
 
3.4%
passat 33
 
3.2%
a5 32
 
3.1%
legacy 32
 
3.1%
pathfinder 31
 
3.0%
Other values (31) 685
65.7%

Most occurring characters

ValueCountFrequency (%)
a 492
 
9.5%
e 428
 
8.3%
r 392
 
7.6%
o 238
 
4.6%
i 235
 
4.5%
t 185
 
3.6%
l 179
 
3.5%
n 178
 
3.4%
M 168
 
3.2%
s 157
 
3.0%
Other values (42) 2526
48.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3351
64.7%
Uppercase Letter 1207
 
23.3%
Decimal Number 577
 
11.1%
Space Separator 43
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 492
14.7%
e 428
12.8%
r 392
11.7%
o 238
 
7.1%
i 235
 
7.0%
t 185
 
5.5%
l 179
 
5.3%
n 178
 
5.3%
s 157
 
4.7%
d 113
 
3.4%
Other values (13) 754
22.5%
Uppercase Letter
ValueCountFrequency (%)
M 168
13.9%
C 133
11.0%
A 125
10.4%
X 87
 
7.2%
F 76
 
6.3%
R 75
 
6.2%
L 72
 
6.0%
P 64
 
5.3%
S 52
 
4.3%
E 51
 
4.2%
Other values (10) 304
25.2%
Decimal Number
ValueCountFrequency (%)
5 144
25.0%
0 137
23.7%
3 118
20.5%
9 80
13.9%
2 28
 
4.9%
4 27
 
4.7%
1 27
 
4.7%
6 16
 
2.8%
Space Separator
ValueCountFrequency (%)
43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4558
88.0%
Common 620
 
12.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 492
 
10.8%
e 428
 
9.4%
r 392
 
8.6%
o 238
 
5.2%
i 235
 
5.2%
t 185
 
4.1%
l 179
 
3.9%
n 178
 
3.9%
M 168
 
3.7%
s 157
 
3.4%
Other values (33) 1906
41.8%
Common
ValueCountFrequency (%)
5 144
23.2%
0 137
22.1%
3 118
19.0%
9 80
12.9%
43
 
6.9%
2 28
 
4.5%
4 27
 
4.4%
1 27
 
4.4%
6 16
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 492
 
9.5%
e 428
 
8.3%
r 392
 
7.6%
o 238
 
4.6%
i 235
 
4.5%
t 185
 
3.6%
l 179
 
3.5%
n 178
 
3.4%
M 168
 
3.2%
s 157
 
3.0%
Other values (42) 2526
48.8%

auto_year
Real number (ℝ)

Distinct21
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.103
Minimum1995
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-12-28T18:40:25.033833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1995
5-th percentile1995
Q12000
median2005
Q32010
95-th percentile2014
Maximum2015
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.0158608
Coefficient of variation (CV)0.0030002752
Kurtosis-1.1718678
Mean2005.103
Median Absolute Deviation (MAD)5
Skewness-0.048288807
Sum2005103
Variance36.190582
MonotonicityNot monotonic
2025-12-28T18:40:25.086202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1995 56
 
5.6%
1999 55
 
5.5%
2005 54
 
5.4%
2006 53
 
5.3%
2011 53
 
5.3%
2007 52
 
5.2%
2003 51
 
5.1%
2009 50
 
5.0%
2010 50
 
5.0%
2013 49
 
4.9%
Other values (11) 477
47.7%
ValueCountFrequency (%)
1995 56
5.6%
1996 37
3.7%
1997 46
4.6%
1998 40
4.0%
1999 55
5.5%
2000 42
4.2%
2001 42
4.2%
2002 49
4.9%
2003 51
5.1%
2004 39
3.9%
ValueCountFrequency (%)
2015 47
4.7%
2014 44
4.4%
2013 49
4.9%
2012 46
4.6%
2011 53
5.3%
2010 50
5.0%
2009 50
5.0%
2008 45
4.5%
2007 52
5.2%
2006 53
5.3%
Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
1
581 
3
358 
4
 
31
2
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

Length

2025-12-28T18:40:25.141332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-28T18:40:25.189691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

Most occurring characters

ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 581
58.1%
3 358
35.8%
4 31
 
3.1%
2 30
 
3.0%

bodily_injuries
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
340 
2
332 
1
328 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row2
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

Length

2025-12-28T18:40:25.232874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-28T18:40:25.280438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

Most occurring characters

ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 340
34.0%
2 332
33.2%
1 328
32.8%

witnesses
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
1
258 
2
250 
0
249 
3
243 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row0
3rd row3
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%

Length

2025-12-28T18:40:25.323356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-28T18:40:25.372621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%

Most occurring characters

ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 258
25.8%
2 250
25.0%
0 249
24.9%
3 243
24.3%

property_damage
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.7 KiB
?
360 
NO
338 
YES
302 

Length

Max length3
Median length2
Mean length1.942
Min length1

Characters and Unicode

Total characters1942
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYES
2nd row?
3rd rowNO
4th row?
5th rowNO

Common Values

ValueCountFrequency (%)
? 360
36.0%
NO 338
33.8%
YES 302
30.2%

Length

2025-12-28T18:40:25.424007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-28T18:40:25.478199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
360
36.0%
no 338
33.8%
yes 302
30.2%

Most occurring characters

ValueCountFrequency (%)
? 360
18.5%
N 338
17.4%
O 338
17.4%
Y 302
15.6%
E 302
15.6%
S 302
15.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1582
81.5%
Other Punctuation 360
 
18.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 338
21.4%
O 338
21.4%
Y 302
19.1%
E 302
19.1%
S 302
19.1%
Other Punctuation
ValueCountFrequency (%)
? 360
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1582
81.5%
Common 360
 
18.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 338
21.4%
O 338
21.4%
Y 302
19.1%
E 302
19.1%
S 302
19.1%
Common
ValueCountFrequency (%)
? 360
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1942
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
? 360
18.5%
N 338
17.4%
O 338
17.4%
Y 302
15.6%
E 302
15.6%
S 302
15.6%
Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.7 KiB
?
343 
NO
343 
YES
314 

Length

Max length3
Median length2
Mean length1.971
Min length1

Characters and Unicode

Total characters1971
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYES
2nd row?
3rd rowNO
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
? 343
34.3%
NO 343
34.3%
YES 314
31.4%

Length

2025-12-28T18:40:25.527079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-28T18:40:25.581108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
343
34.3%
no 343
34.3%
yes 314
31.4%

Most occurring characters

ValueCountFrequency (%)
? 343
17.4%
N 343
17.4%
O 343
17.4%
Y 314
15.9%
E 314
15.9%
S 314
15.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1628
82.6%
Other Punctuation 343
 
17.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 343
21.1%
O 343
21.1%
Y 314
19.3%
E 314
19.3%
S 314
19.3%
Other Punctuation
ValueCountFrequency (%)
? 343
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1628
82.6%
Common 343
 
17.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 343
21.1%
O 343
21.1%
Y 314
19.3%
E 314
19.3%
S 314
19.3%
Common
ValueCountFrequency (%)
? 343
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1971
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
? 343
17.4%
N 343
17.4%
O 343
17.4%
Y 314
15.9%
E 314
15.9%
S 314
15.9%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
False
753 
True
247 
ValueCountFrequency (%)
False 753
75.3%
True 247
 
24.7%
2025-12-28T18:40:25.627472image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

total_claim_amount
Real number (ℝ)

HIGH CORRELATION 

Distinct763
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52761.94
Minimum100
Maximum114920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-12-28T18:40:25.676871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile4320
Q141812.5
median58055
Q370592.5
95-th percentile88413
Maximum114920
Range114820
Interquartile range (IQR)28780

Descriptive statistics

Standard deviation26401.533
Coefficient of variation (CV)0.50038974
Kurtosis-0.45408143
Mean52761.94
Median Absolute Deviation (MAD)13855
Skewness-0.59458199
Sum52761940
Variance6.9704095 × 108
MonotonicityNot monotonic
2025-12-28T18:40:25.741780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59400 5
 
0.5%
2640 4
 
0.4%
70400 4
 
0.4%
4320 4
 
0.4%
44200 4
 
0.4%
75400 4
 
0.4%
60600 4
 
0.4%
3190 4
 
0.4%
58500 4
 
0.4%
70290 4
 
0.4%
Other values (753) 959
95.9%
ValueCountFrequency (%)
100 1
 
0.1%
1920 1
 
0.1%
2160 1
 
0.1%
2250 1
 
0.1%
2400 1
 
0.1%
2520 1
 
0.1%
2640 4
0.4%
2700 2
0.2%
2800 1
 
0.1%
2860 1
 
0.1%
ValueCountFrequency (%)
114920 1
0.1%
112320 1
0.1%
108480 1
0.1%
108030 1
0.1%
107900 1
0.1%
105820 1
0.1%
105040 1
0.1%
104610 1
0.1%
103560 1
0.1%
101860 1
0.1%

injury_claim
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct638
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7433.42
Minimum0
Maximum21450
Zeros25
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-12-28T18:40:25.808683image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile450
Q14295
median6775
Q311305
95-th percentile15662
Maximum21450
Range21450
Interquartile range (IQR)7010

Descriptive statistics

Standard deviation4880.9519
Coefficient of variation (CV)0.65662264
Kurtosis-0.76308706
Mean7433.42
Median Absolute Deviation (MAD)3705
Skewness0.26481088
Sum7433420
Variance23823691
MonotonicityNot monotonic
2025-12-28T18:40:25.870336image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
 
2.5%
640 7
 
0.7%
480 7
 
0.7%
660 5
 
0.5%
580 5
 
0.5%
13520 5
 
0.5%
1180 5
 
0.5%
860 5
 
0.5%
6340 5
 
0.5%
780 5
 
0.5%
Other values (628) 926
92.6%
ValueCountFrequency (%)
0 25
2.5%
10 1
 
0.1%
220 1
 
0.1%
250 1
 
0.1%
280 2
 
0.2%
290 1
 
0.1%
300 3
 
0.3%
330 2
 
0.2%
350 1
 
0.1%
360 1
 
0.1%
ValueCountFrequency (%)
21450 1
0.1%
21330 1
0.1%
20700 1
0.1%
19020 1
0.1%
18520 1
0.1%
18220 1
0.1%
18180 1
0.1%
18080 1
0.1%
18000 1
0.1%
17880 1
0.1%

property_claim
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct626
Distinct (%)62.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7399.57
Minimum0
Maximum23670
Zeros19
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-12-28T18:40:25.932632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile450
Q14445
median6750
Q310885
95-th percentile15540
Maximum23670
Range23670
Interquartile range (IQR)6440

Descriptive statistics

Standard deviation4824.7262
Coefficient of variation (CV)0.65202791
Kurtosis-0.37638631
Mean7399.57
Median Absolute Deviation (MAD)3290
Skewness0.37816878
Sum7399570
Variance23277983
MonotonicityNot monotonic
2025-12-28T18:40:26.074369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
1.9%
860 6
 
0.6%
480 5
 
0.5%
660 5
 
0.5%
10000 5
 
0.5%
640 5
 
0.5%
650 5
 
0.5%
11080 5
 
0.5%
840 4
 
0.4%
5310 4
 
0.4%
Other values (616) 937
93.7%
ValueCountFrequency (%)
0 19
1.9%
20 1
 
0.1%
240 1
 
0.1%
250 1
 
0.1%
260 1
 
0.1%
280 3
 
0.3%
290 2
 
0.2%
300 3
 
0.3%
320 3
 
0.3%
330 1
 
0.1%
ValueCountFrequency (%)
23670 1
0.1%
21810 1
0.1%
21630 1
0.1%
21580 1
0.1%
21240 1
0.1%
20550 1
0.1%
20310 1
0.1%
20280 1
0.1%
19950 1
0.1%
19650 1
0.1%

vehicle_claim
Real number (ℝ)

HIGH CORRELATION 

Distinct726
Distinct (%)72.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37928.95
Minimum70
Maximum79560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-12-28T18:40:26.138805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile3273.5
Q130292.5
median42100
Q350822.5
95-th percentile63094.5
Maximum79560
Range79490
Interquartile range (IQR)20530

Descriptive statistics

Standard deviation18886.253
Coefficient of variation (CV)0.49793767
Kurtosis-0.44657292
Mean37928.95
Median Absolute Deviation (MAD)9840
Skewness-0.62109793
Sum37928950
Variance3.5669055 × 108
MonotonicityNot monotonic
2025-12-28T18:40:26.203996image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5040 7
 
0.7%
3360 6
 
0.6%
52080 5
 
0.5%
4720 5
 
0.5%
3600 5
 
0.5%
44800 5
 
0.5%
33600 5
 
0.5%
42720 4
 
0.4%
41580 4
 
0.4%
35000 4
 
0.4%
Other values (716) 950
95.0%
ValueCountFrequency (%)
70 1
0.1%
1440 2
0.2%
1680 2
0.2%
1750 1
0.1%
1760 1
0.1%
1800 1
0.1%
1960 2
0.2%
1980 1
0.1%
2030 1
0.1%
2080 1
0.1%
ValueCountFrequency (%)
79560 1
0.1%
77760 1
0.1%
77670 2
0.2%
76400 1
0.1%
76000 1
0.1%
75600 1
0.1%
75530 1
0.1%
74790 1
0.1%
73620 1
0.1%
73260 1
0.1%

Interactions

2025-12-28T18:40:23.116500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:19.262774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:19.838568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:20.393065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:20.953785image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:21.472678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:21.969826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:22.496970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:23.184667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:19.345215image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:19.920236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:20.456942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:21.022537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:21.538133image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:22.033608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:22.560938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:23.248343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:19.411660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:19.986349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:20.516098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:21.086804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:21.598583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:22.092103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:22.622646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:23.309042image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:19.476970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:20.055771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:20.571758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:21.148289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:21.657085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:22.148166image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:22.696165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:23.377867image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:19.548177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:20.134005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:20.635973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:21.217444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:21.724094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:22.212549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:22.764946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:23.442039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:19.613547image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:20.201979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:20.696173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:21.282559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:21.786584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:22.317381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:22.936362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:23.502627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:19.677575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:20.265436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:20.751903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:21.343573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:21.845159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:22.376403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:22.996651image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:23.563407image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:19.747865image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:20.327570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:20.808143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:21.404446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:21.903875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:22.433404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2025-12-28T18:40:23.052988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2025-12-28T18:40:26.273477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
auto_makeauto_modelauto_yearbodily_injuriesfraud_reportedinjury_claiminsured_ageinsured_education_levelinsured_hobbiesinsured_occupationinsured_relationshipinsured_sexnumber_of_vehicles_involvedpolice_report_availablepolicy_annual_premiumpolicy_deductableproperty_claimproperty_damagetotal_claim_amountumbrella_limitvehicle_claimwitnesses
auto_make1.0000.9870.0050.0000.028-0.0380.0160.0380.0470.0000.0120.0000.0410.0000.0080.000-0.0430.000-0.0630.006-0.0660.000
auto_model0.9871.0000.0320.0000.0930.0450.0120.0280.0510.0370.0230.0000.0870.035-0.0300.0000.0610.0000.0420.0310.0300.000
auto_year0.0050.0321.0000.0440.000-0.0190.0060.0000.0000.0380.0600.0440.0330.047-0.0300.000-0.0080.000-0.0330.012-0.0410.018
bodily_injuries0.0000.0000.0441.0000.0000.048-0.0190.0090.0000.0940.0000.0000.0000.0000.0210.0180.0330.0170.0590.0420.0580.017
fraud_reported0.0280.0930.0000.0001.0000.0880.0040.0000.3790.0680.0200.0000.0300.000-0.0150.0000.1340.0780.1390.0600.1430.056
injury_claim-0.0380.045-0.0190.0480.0881.0000.0740.0000.0370.0330.0420.0000.2010.033-0.0190.0530.5690.0000.792-0.0470.6840.000
insured_age0.0160.0120.006-0.0190.0040.0741.0000.0000.0000.0000.0370.0800.0810.0000.0310.0000.0560.0000.0650.0020.0510.046
insured_education_level0.0380.0280.0000.0090.0000.0000.0001.0000.0000.0410.0420.0000.0200.050-0.0180.0000.0560.0210.073-0.0160.0710.060
insured_hobbies0.0470.0510.0000.0000.3790.0370.0000.0001.0000.0500.0270.0000.0000.094-0.0240.000-0.0010.0000.003-0.036-0.0070.034
insured_occupation0.0000.0370.0380.0940.0680.0330.0000.0410.0501.0000.0540.0000.0000.0000.0280.081-0.0020.0000.0100.0200.0030.031
insured_relationship0.0120.0230.0600.0000.0200.0420.0370.0420.0270.0541.0000.0000.0000.0000.0070.0000.0150.0000.0060.088-0.0110.000
insured_sex0.0000.0000.0440.0000.0000.0000.0800.0000.0000.0000.0001.0000.0000.0000.0350.000-0.0070.000-0.0210.011-0.0320.000
number_of_vehicles_involved0.0410.0870.0330.0000.0300.2010.0810.0200.0000.0000.0000.0001.0000.000-0.0440.0480.2220.0000.210-0.0250.2010.000
police_report_available0.0000.0350.0470.0000.0000.0330.0000.0500.0940.0000.0000.0000.0001.0000.0150.000-0.0140.026-0.012-0.053-0.0040.000
policy_annual_premium0.008-0.030-0.0300.021-0.015-0.0190.031-0.018-0.0240.0280.0070.035-0.0440.0151.0000.052-0.0040.056-0.002-0.0010.0070.036
policy_deductable0.0000.0000.0000.0180.0000.0530.0000.0000.0000.0810.0000.0000.0480.0000.0521.0000.0570.0000.0200.0020.0040.043
property_claim-0.0430.061-0.0080.0330.1340.5690.0560.056-0.001-0.0020.015-0.0070.222-0.014-0.0040.0571.0000.0270.798-0.0180.6930.000
property_damage0.0000.0000.0000.0170.0780.0000.0000.0210.0000.0000.0000.0000.0000.0260.0560.0000.0271.0000.053-0.0610.0470.000
total_claim_amount-0.0630.042-0.0330.0590.1390.7920.0650.0730.0030.0100.006-0.0210.210-0.012-0.0020.0200.7980.0531.000-0.0410.9650.000
umbrella_limit0.0060.0310.0120.0420.060-0.0470.002-0.016-0.0360.0200.0880.011-0.025-0.053-0.0010.002-0.018-0.061-0.0411.000-0.0380.000
vehicle_claim-0.0660.030-0.0410.0580.1430.6840.0510.071-0.0070.003-0.011-0.0320.201-0.0040.0070.0040.6930.0470.965-0.0381.0000.000
witnesses0.0000.0000.0180.0170.0560.0000.0460.0600.0340.0310.0000.0000.0000.0000.0360.0430.0000.0000.0000.0000.0001.000

Missing values

2025-12-28T18:40:23.673151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-28T18:40:23.838805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

policy_annual_premiumpolicy_deductableumbrella_limitinsured_ageinsured_sexinsured_education_levelinsured_occupationinsured_relationshipinsured_hobbiesauto_makeauto_modelauto_yearnumber_of_vehicles_involvedbodily_injurieswitnessesproperty_damagepolice_report_availablefraud_reportedtotal_claim_amountinjury_claimproperty_claimvehicle_claim
01406.911000048MALEMDcraft-repairhusbandsleepingSaab92x2004112YESYESY71610.06510.013020.052080.0
11197.222000500000042MALEMDmachine-op-inspctother-relativereadingMercedesE4002007100??Y5070.0780.0780.03510.0
21413.142000500000029FEMALEPhDsalesown-childboard-gamesDodgeRAM2007323NONON34650.07700.03850.023100.0
31415.742000600000041FEMALEPhDarmed-forcesunmarriedboard-gamesChevroletTahoe2014112?NOY63400.06340.06340.050720.0
41583.911000600000044MALEAssociatesalesunmarriedboard-gamesAccuraRSX2009101NONON6500.01300.0650.04550.0
51351.101000039FEMALEPhDtech-supportunmarriedbungie-jumpingSaab952003302NONOY64100.06410.06410.051280.0
61333.351000034MALEPhDprof-specialtyhusbandboard-gamesNissanPathfinder2012300??N78650.021450.07150.050050.0
71137.031000037MALEAssociatetech-supportunmarriedbase-jumpingAudiA52015322?YESN51590.09380.09380.032830.0
81442.99500033FEMALEPhDother-serviceown-childgolfToyotaCamry2012111NOYESN27700.02770.02770.022160.0
91315.68500042MALEPhDpriv-house-servwifecampingSaab92x1996121NO?N42300.04700.04700.032900.0
policy_annual_premiumpolicy_deductableumbrella_limitinsured_ageinsured_sexinsured_education_levelinsured_occupationinsured_relationshipinsured_hobbiesauto_makeauto_modelauto_yearnumber_of_vehicles_involvedbodily_injurieswitnessesproperty_damagepolice_report_availablefraud_reportedtotal_claim_amountinjury_claimproperty_claimvehicle_claim
9901564.43500300000043FEMALEMDprof-specialtyunmarriedmoviesJeepGrand Cherokee2013122?YESN34290.03810.03810.026670.0
9911280.881000044MALEMDother-serviceother-relativebasketballAccuraTL2002101NONON46980.00.05220.041760.0
992722.66500026MALEMDexec-managerialhusbandcampingNissanPathfinder2010312YESYESN36700.03670.07340.025690.0
9931235.141000028MALEMDexec-managerialhusbandcampingVolkswagenPassat2012301??N60200.06020.06020.048160.0
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